Abstract: Objectives: With the rapid development of low-altitude economy and the widespread application of unmanned aerial vehicle (UAV) technology, traditional manual-controlled single-UAV operation modes can no longer meet the requirements of large-scale, periodic, and high-efficiency surveying and mapping tasks. This paper, for the first time, proposes the concept of a new surveying and mapping infrastructure based on UAV dock networks (DN-SMI). It aims to define its core characteristics as an infrastructure, including networked networking capability, large-scale support capability, platform-based service capability, and unified spatiotemporal benchmark, and to establish a systematic technical framework and implementation scheme for DN-SMI. Methods: Based on the evolution of multi-platform observation systems (satellite, aerial, low-altitude UAV, and ground platforms), this paper first identifies the limitations of existing UAV mapping modes and the necessity of upgrading to a dock-network-based infrastructure. It then defines the core characteristics of DN-SMI: networked deployment of intelligent UAV docks enabling wide-coverage and rapid-response operations; large-scale support through cloud-edge-end collaboration for massive data processing; platform-based services supporting plug-and-play multi-sensors and on-demand data products; and unified spatiotemporal benchmark via integration with CORS. A four-layer technical framework is constructed, covering the hardware layer (UAV platforms, standardized payload interfaces, and grid-deployed intelligent docks), communication layer (5G-A/6G and IoT-based low-altitude ubiquitous connectivity), processing layer (offline SfM and online SLAM for 3D reconstruction, plus intelligent image interpretation for thematic mapping), and service layer (crowdsourced task distribution, automated processing pipeline, and visualized product delivery). Key technologies are elaborated from four aspects: multi-UAV collaborative data acquisition (including optimized views photogrammetry for complex urban scenes), large-scale scene 3D reconstruction (efficient image matching, parallel SfM, distributed 3D Gaussian splatting, and real-time photogrammetry), thematic information extraction (accurate building polygonal mapping and parametric wireframe reconstruction), and an integrated service platform with crowdsourcing mechanism. Finally, typical application scenarios are demonstrated, including urban governance and renewal, engineering construction monitoring, disaster emergency response, and environmental protection monitoring. Results: The proposed DN-SMI concept and technical framework address the bottlenecks of traditional UAV mapping in large-scale, periodic, and high-timeliness tasks. The four-layer architecture and key technologies have been validated through practical deployments, demonstrating significant improvements in data acquisition efficiency, 3D reconstruction quality, and service delivery standardization. The crowdsourced service platform supports on-demand, traceable, and standardized geospatial product delivery, effectively lowering the threshold for high-precision mapping and providing reliable support for urban digital twins, low-altitude economy, and smart city management. Conclusions: The DN-SMI represents a fundamental shift of UAV surveying and mapping from a single data collection tool to a networked, intelligent, and serviceoriented infrastructure. The proposed concept, core characteristics, four-layer technical framework, and key technologies provide systematic theoretical guidance for technological development and engineering practice. Future work should focus on enhancing robustness under complex environments, improving legality (airspace compliance, data security, privacy protection), and ensuring universality (cost-effective deployment and cross-regional interoperability), so as to promote DN-SMI as a reliable, lawful, and ubiquitous spatiotemporal information infrastructure for the digital economy and low-altitude economy.